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The treatment of tannery effluent is of great interest as it contains a complex mixture of pollutants, primarily chromium. The disposal of this wastewater can have adverse effects on the environment and aquatic life, which is an emerging problem for the environment. In this work, electrocoagulation is used to remove chromium from real tannery wastewater, focusing on performance optimization and sludge characterization. Electrocoagulation experiments were conducted using an electrochemical cell with iron electrodes immersed in a specific volume of tannery wastewater. Operating parameters, such as the initial chromium concentration, pH and current density as well as power consumption were evaluated to determine optimum chromium removal. The optimization was performed using Response Surface Methodology combined with central composite design. Analysis of variance (ANOVA) was used to determine the response, residual, probability, 3D surface and contour plots. The maximum chromium removal was 100% at the optimum values of 13 mA/cm2, 7 and 750 ppm for current density, pH and concentration, respectively.
Nahid M. Genawi; Mohamed H. Ibrahim; Muftah H. El-Naas; Awad E. Alshaik. Chromium Removal from Tannery Wastewater by Electrocoagulation: Optimization and Sludge Characterization. Water 2020, 12, 1374 .
AMA StyleNahid M. Genawi, Mohamed H. Ibrahim, Muftah H. El-Naas, Awad E. Alshaik. Chromium Removal from Tannery Wastewater by Electrocoagulation: Optimization and Sludge Characterization. Water. 2020; 12 (5):1374.
Chicago/Turabian StyleNahid M. Genawi; Mohamed H. Ibrahim; Muftah H. El-Naas; Awad E. Alshaik. 2020. "Chromium Removal from Tannery Wastewater by Electrocoagulation: Optimization and Sludge Characterization." Water 12, no. 5: 1374.
Steelmaking is an energy-intensive process that generates considerable amounts of by-products and wastes, which often pose major environmental and economic challenges to the steel-making industry. One of these by-products is steel dust that is produced during the separation of impurities in the smelting and refining of metals in steel-making furnaces. In this study, electric arc furnace (EAF) dust has been evaluated as a potential, low-cost additive to increase the viscosity and weight of drilling muds. Currently, the cost of drilling operations typically accounts for 50 to 80% of the exploration costs and about 30 to 80% of the subsequent field development costs. Utilization of steelmaking waste in drilling fluids formulations is aimed to produce new and optimized water-based drilling formulations, which is expected to reduce the amount of bentonite and other viscosifier additives used in the drilling formulations. The results showed that in a typical water-based drilling fluid of 8.6 ppg (1030.51 kg/m3), the amount of standard drilling grade bentonite could be reduced by 30 wt.% with the addition of the proposed new additive to complete the required mud weight. The mixture proved to be stable with no phase separation.
Musaab I. Magzoub; Mohamed H. Ibrahim; Mustafa S. Nasser; Muftah H. El-Naas; Mahmood Amani. Utilization of Steel-Making Dust in Drilling Fluids Formulations. Processes 2020, 8, 538 .
AMA StyleMusaab I. Magzoub, Mohamed H. Ibrahim, Mustafa S. Nasser, Muftah H. El-Naas, Mahmood Amani. Utilization of Steel-Making Dust in Drilling Fluids Formulations. Processes. 2020; 8 (5):538.
Chicago/Turabian StyleMusaab I. Magzoub; Mohamed H. Ibrahim; Mustafa S. Nasser; Muftah H. El-Naas; Mahmood Amani. 2020. "Utilization of Steel-Making Dust in Drilling Fluids Formulations." Processes 8, no. 5: 538.
Data-driven models are essential tools for the development of surrogate models that can be used for the design, operation, and optimization of industrial processes. One approach of developing surrogate models is through the use of input–output data obtained from a process simulator. To enhance the model robustness, proper sampling techniques are required to cover the entire domain of the process variables uniformly. In the present work, Monte Carlo with pseudo-random samples as well as Latin hypercube samples and quasi-Monte Carlo samples with Hammersley Sequence Sampling (HSS) are generated. The sampled data obtained from the process simulator are fitted to neural networks for generating a surrogate model. An illustrative case study is solved to predict the gas stabilization unit performance. From the developed surrogate models to predict process data, it can be concluded that of the different sampling methods, Latin hypercube sampling and HSS have better performance than the pseudo-random sampling method for designing the surrogate model. This argument is based on the maximum absolute value, standard deviation, and the confidence interval for the relative average error as obtained from different sampling techniques.
Mohamed Ibrahim; Saad Al-Sobhi; Rajib Mukherjee; Ahmed AlNouss. Impact of Sampling Technique on the Performance of Surrogate Models Generated with Artificial Neural Network (ANN): A Case Study for a Natural Gas Stabilization Unit. Energies 2019, 12, 1906 .
AMA StyleMohamed Ibrahim, Saad Al-Sobhi, Rajib Mukherjee, Ahmed AlNouss. Impact of Sampling Technique on the Performance of Surrogate Models Generated with Artificial Neural Network (ANN): A Case Study for a Natural Gas Stabilization Unit. Energies. 2019; 12 (10):1906.
Chicago/Turabian StyleMohamed Ibrahim; Saad Al-Sobhi; Rajib Mukherjee; Ahmed AlNouss. 2019. "Impact of Sampling Technique on the Performance of Surrogate Models Generated with Artificial Neural Network (ANN): A Case Study for a Natural Gas Stabilization Unit." Energies 12, no. 10: 1906.
Carbon capture and sequestration (CCS) is taking the lead as a means for mitigating climate change. It is considered a crucial bridging technology, enabling carbon dioxide (CO2) emissions from fossil fuels to be reduced while the energy transition to renewable sources is taking place. CCS includes a portfolio of technologies that can possibly capture vast amounts of CO2 per year. Mineral carbonation is evolving as a possible candidate to sequester CO2 from medium-sized emissions point sources. It is the only recognized form of permanent CO2 storage with no concerns regarding CO2 leakage. It is based on the principles of natural rock weathering, where the CO2 dissolved in rainwater reacts with alkaline rocks to form carbonate minerals. The active alkaline elements (Ca/Mg) are the fundamental reactants for mineral carbonation reaction. Although the reaction is thermodynamically favored, it takes place over a large time scale. The challenge of mineral carbonation is to offset this limitation by accelerating the carbonation reaction with minimal energy and feedstock consumption. Calcium and magnesium silicates are generally selected for carbonation due to their abundance in nature. Industrial waste residues emerge as an alternative source of carbonation minerals that have higher reactivity than natural minerals; they are also inexpensive and readily available in proximity to CO2 emitters. In addition, the environmental stability of the industrial waste is often enhanced as they undergo carbonation. Recently, direct mineral carbonation has been investigated significantly due to its applicability to CO2 capture and storage. This review outlines the main research work carried out over the last few years on direct mineral carbonation process utilizing steel-making waste, with emphasis on recent research achievements and potentials for future research.
Mohamed H. Ibrahim; Muftah H. El-Naas; Abdelbaki Benamor; Saad S. Al-Sobhi; Zhien Zhang. Carbon Mineralization by Reaction with Steel-Making Waste: A Review. Processes 2019, 7, 115 .
AMA StyleMohamed H. Ibrahim, Muftah H. El-Naas, Abdelbaki Benamor, Saad S. Al-Sobhi, Zhien Zhang. Carbon Mineralization by Reaction with Steel-Making Waste: A Review. Processes. 2019; 7 (2):115.
Chicago/Turabian StyleMohamed H. Ibrahim; Muftah H. El-Naas; Abdelbaki Benamor; Saad S. Al-Sobhi; Zhien Zhang. 2019. "Carbon Mineralization by Reaction with Steel-Making Waste: A Review." Processes 7, no. 2: 115.